公开(公告)号：US20240405565A1

公开(公告)日：2024-12-05

申请号：US18327594

申请日：2023-06-01

Applicant: General Electric Company

Inventor： Bojun Feng ,  Nurali Virani ,  Honggang Wang ,  Benoit Christophe ,  Kiran Kumar Pratapagiri

IPC: H02J3/38 ,  H02J3/00

Abstract: A system for predicting performance of electric power generation and delivery systems is provided. The system includes a computing device including at least one processor in communication with at least one memory. The at least one processor is programmed to store a first plurality of attribute data for a plurality of measured assets attached to a grid, store a plurality of constraints for matching measured assets to unmeasured assets, receive a second plurality of attribute data for an unmeasured asset attached to the grid, compare the first plurality of attribute data to the second plurality of attribute data and the plurality of constraints associated with the unmeasured asset, determine a measured asset of the plurality of measured assets to assign to the unmeasured asset based on the comparison, and determine a performance forecast for the unmeasured asset based on a power performance of the determined measured asset.

公开(公告)号：US20240054348A1

公开(公告)日：2024-02-15

申请号：US18327619

申请日：2023-06-01

Applicant: General Electric Company

Inventor： Yiwei Fu ,  Nurali Virani ,  Honggang Wang ,  Benoit Christophe

IPC: G06N3/0895

CPC classification number: G06N3/0895

Abstract: A system includes a computing device including at least one processor in communication with at least one memory. The at least one processor is programmed to (a) store a plurality of historical time series data; (b) randomly select a sequence; (c) randomly select a mask length for a mask for the selected sequence; (d) apply the mask to the selected sequence, wherein the mask is applied to the plurality of forecast variables in the selected sequence; (e) execute a model with the masked selected sequence to generate predictions for the masked forecast variables; (f) compare the predictions for the masked forecast variables to the actual forecast variables in the selected sequence; (g) determine if convergence occurs based upon the comparison; and (h) if convergence has not occurred, update one or more parameters of the model and return to step b.